System and method for visual art streaming runtime platform

ABSTRACT

The Loupe system creates a display and channel creation capability for art images to be presented to a user to optimize the user experience in viewing art images delivered onto digital displays, TVs and other screens facilitating the artwork transition with and without human interaction. Art imagery to be streamed includes imagery such as, but not limited to, filtered and personalized streams of art imagery. The Loupe system recommendations engine utilizes both human and machine curated data to determine factors of art images that optimize and extend the user time spent on viewing the images. The Loupe system gathers data that is analyzed through machine learning and AI algorithms to inform recommendations and select art images to optimize the user experience. The user may purchase fine art prints or select originals of the artwork image displayed, if available for sale, from the Loupe integrated electronic marketplace.

CLAIM TO PRIORITY

This application claims under 35 U.S.C. § 120, the benefit of theApplication 62/771,634, filed Nov. 26, 2018, titled “Loupe Art Platformwith Integrated Online Marketplace Powered by the Visual Art DNA Engine”which is hereby incorporated by reference in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND

Historically, art display systems attempt to display art images in acatalogue fashion for a user to page through and discover art imagesthat they may be interested in purchasing. Such systems provide littleassistance to a user in winnowing down the number of art images in whichthey might have any interest. In such systems previous classificationsystems are more fixed.

Other art display systems have attempted to provide a more automatedprocess for selecting images of interest to a user throughclassification of images based upon one or more factors related to eachimage. The classification places art imagery into categories that arehuman defined and are usually based upon art history, geography, andpolitical influence. Previous implementations create a category matchingsystem that can sometimes present art imagery to a user in which theyhave an interest, but along with many other items in a category in whichthe user has little or no interest. This more automated process is stillhighly dependent upon the catalogue implementation, although thecatalogue presented to a user is reduced into categories that a user,hopefully, finds more interesting. In many established systems, userscontinue to be presented with art imagery that is of little interest butthat is included in the category based upon a category key. Thus, withexisting systems a user is largely required to select art imagery frommany images that hold little interest to the user.

Alternatively, in currently existing art image delivery systems, a usercould select an option to request all art images resident in aparticular categorization and have these images delivered to them.Although such categorization does provide for imagery a user prefers asopposed to an entire catalogue, there are undoubtedly many images in adefined category that a user would not select or in which they havelittle interest.

When streaming art images to a display, therefore, a user is generallyrequired to select the images to be displayed in the streamed images.Even in systems where an automated classification technique is utilizedto reduce the number of images based upon a user's expressed interests,there are often user interests that remain unexpressed and theexperience is less than optimum for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain illustrative embodiments illustrating organization and method ofoperation, together with objects and advantages may be best understoodby reference to the detailed description that follows taken inconjunction with the accompanying drawings in which:

FIG. 1 is a view of the Loupe system architecture consistent withcertain embodiments of the present invention.

FIG. 2 is a view of the Loupe system content identification processconsistent with certain embodiments of the present invention.

FIG. 3 is a view of the Loupe system player control consistent withcertain embodiments of the present invention.

FIG. 4 is a view of the Loupe system home page consistent with certainembodiments of the present invention.

FIG. 5 is a view of the Loupe system content presentation consistentwith certain embodiments of the present invention.

FIG. 6 is a view of the Loupe system stream by color capability displayconsistent with certain embodiments of the present invention.

FIG. 7 is a view of the Loupe system user behavior verificationconsistent with certain embodiments of the present invention.

FIG. 8 is a view of the streaming selection menu of the Loupe systemconsistent with certain embodiments of the present invention.

FIG. 9 is a view of a selected streaming display presented to a userconsistent with certain embodiments of the present invention.

FIG. 10 is a view of the Loupe system pause and purchase capabilityconsistent with certain embodiments of the present invention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail specific embodiments, with the understanding that the presentdisclosure of such embodiments is to be considered as an example of theprinciples and not intended to limit the invention to the specificembodiments shown and described. In the description below, likereference numerals are used to describe the same, similar orcorresponding parts in the several views of the drawings.

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term “plurality”, as used herein, is defined as two or morethan two. The term “another”, as used herein, is defined as at least asecond or more. The terms “including” and/or “having”, as used herein,are defined as comprising (i.e., open language). The term “coupled”, asused herein, is defined as connected, although not necessarily directly,and not necessarily mechanically.

Reference throughout this document to “one embodiment”, “certainembodiments”, “an embodiment” or similar terms means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the presentinvention. Thus, the appearances of such phrases or in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments without limitation.

In an embodiment, the use of the term “CSS” throughout this documentwill refer to Cascading Style Sheets, which may be used to control theappearance of the artwork presented to one or more users on a displaydevice.

In an embodiment, the use of the term “private channel” shall refer to achannel provided to a commercial user or client that is accessed bypresentation of login credentials.

In an embodiment, the use of the term “art image characterization” shallrefer to the collection of characteristics that enable either a human ormachine selection of art images that have greater similarity to oneanother than to other art images in a catalogue of art images.

In an embodiment, the use of the term “system training” refers to thefeedback of human curation in training a learning system to recognizeart image similarities and differences so as to enhance art imagecharacterization for art images that are not a part of any training setor art image database used for training.

The Loupe system is designed and structured as a four-part system todeliver a personalized experience that satisfies the user's visual tastefor representations of art objects, as well as to satisfy the user'staste for a continuous complementary visual experience to theirstreaming music service. The first part of the system comprises acategorization of the art images to be presented using a proprietarymethodology to describe the essence of the visual art images. This artimage characterization provides the categorization for art images suchthat a learning system may collect images that are within the similarityenvelope and concatenate such similar art images into a presentationstream that is suitable for a particular user, display space, hotel,performance hall, outdoor venue, or any other public display of artimages.

A primary goal of the system is to be able to deliver a stream of visualart that is compatible with the user's current taste in visual images.However, the visual art presented to a user may be presented in aninteractive format employing CSS transformation animations of stillimagery through a proprietary art player that meets additionalconditions for use and enjoyment by a user.

In an embodiment, the Loupe system may use a combination of CSSanimation properties and JavaScript based animations to createinteractive CSS animations of art images and achieve the Loupe streamingexperience. Converting CSS based animations along with JavaScript basedanimations into webgl transformations of the art images removes anytendency for the image display to be choppy or jittery. Loupe currentlyuses a library to translate those animations into webgl animations thatcan be offloaded to the GPU. This process step improves the performanceof the system and creates a fluid display of all images provided to auser.

In an alternative embodiment, the Loupe system may provide art imagesfrom streamed video in addition to the presentation of art images as CSSinteractive animations of still images. In this embodiment, the Loupesystem may provide the capability to discover and connect to frameswithin a video and capture the meta-data associated with the fine artimage presented within a video stream. The capture of the meta-datawould then permit the Loupe system to categorize and present to a userone or more fine art images from a video stream. Regardless of the meansfor presentation of art imagery, a user will have the opportunity tointeract with a real time, integrated marketplace where the user mayselect any presented art image for purchase. Not only does the Loupe artplayer support individual preferences and customized recommendations butthe unique desired environment of a near infinite set of public andcommercial locations, that, may, even be changed with time of day, ofyear, holidays, seasons, etc.

The visual art may be delivered on multiple devices having a visualdisplay capability and is presented by the Loupe system in an experiencethat allows the user to discover new things. An important aspect of thesystem is to maintain the engagement of the user with the visualdisplay. To maintain the engagement of the user, which may also bereferred to as the “stickiness” of the experience, it is essential topresent to the user items that may not necessarily conform to thecurrent tastes but may allow the user to discover new things that may beof interest and complement the desired atmosphere of the room whether inthe home or workplace, as well as public or commercial display.

In an embodiment, the determination of “stickiness” with regard to anart image viewing experience may be performed by dividing the number ofdaily active users (DAU) by the number of monthly active users (MAU) toarrive at a percentage value for the stickiness parameter. A higherpercentage value equates to a higher number of users returning to theLoupe system streaming view of art imagery. Thus, in this non-limitingexample, the higher the percentage value, the greater the stickinessvalue for a particular stream of art imagery. The closer the DAU valueis to the MAU value, the greater the stickiness or engagement value isfor the Loupe system art imagery stream. This higher stickiness valuealso may equate to a higher return frequency value for MAUs who areusing the Loupe system viewer application to view one or more artimagery streams or channels. The Loupe system may interrogate theattributes of channels that have the greatest or highest stickinessparameter values and utilize these attributes to increase the stickinessvalue for other channels by replicating those high stickiness parameterattributes to the other channels.

Due to the nature of the Loupe player, the playlist combinations fromthe same database of images generates endless channels. For example,2,000 art images may create as many as 180 channels of unique streams.

Additionally, the system provides a process for receiving and streamingmotion art from artists. This motion art may be ingested through, butare not limited to, an established “pipeline” into the system to produceHLS streams allowing for variable bitrate consumption of the art imageswhen transmitted to a user or venue. The variable bitrate capabilitypermits users and venues to receive content that is tuned to the speedof the Internet connection available to maximize throughput of artimages while minimizing buffering and/or wait times for the display ofart images.

The Loupe system is designed and implemented to curate content anddefine attributes for content. The Loupe system provides a methodologyto persist curation rules, as well as defines and persists attributesdefined for each content item. Attributes may be categorized based onthe area of focus for one or more particular attributes of the contentto be curated and presented to a user. The categorization of attributesis essential in order to score the attributes based on what is importantto the user. The user shall select the “things” of importance, which maythen be mapped to attribute categories. The acceptance and channelassignment for art imagery permits the acceptance of high-resolutionimages of visual artwork in all styles and media, which may includephotographs of sculptures, street art and other multi-dimensionalinstallations, as well as motion/digital art. The selection of artistsfrom whom the Loupe system will accept art imagery begins withqualifying an image on its technical merits. In a non-limiting example,this qualification may take the form of analyzing the artist'sunderstanding of composition, form, color, overall craftsmanship, andother pre-defined parameters important to establishing an artist'screations and how they may be categorized. The art works may then beevaluated according to aesthetic attributes by the chief curator of theLoupe curation team, who is qualified to make such assessments. Artworksubmissions may be received via a combination of direct outreach fromthe chief curator, via a web portal for artist submissions globally, anda wide spectrum of guest curators from galleries, as well as art imageryfrom other art institutions and art collectives of represented artists.In an embodiment, the art imagery onboarding process places the imageryinto a queue for Loupe operations to ingest into a customizedcross-platform ingestion process with final onboarding approval to beprovided by the Loupe system chief experience officer and the Loupecorporate Chief Executive Officer (CEO). Both the chief experienceofficer and the CEO have extensive qualifications for the curation ofart imagery and approach the final selection and sequencing of the artimagery in a similar fashion to music playlist generation to create anexperiential flow to the art imagery presentation that may complementdifferent times of day, different moods to be experienced, and differentlifestyle experiences. In non-limiting examples, the different lifestyleexperiences may include romantic, party, relaxation, workplace, andother lifestyle experience categories.

In an embodiment, the goal of the inclusion of art imagery is to keepthe art imagery content continuously updated, creating new possibilitiesfor experiencing art and creating experiences that will be deemed asvaluable to the consumer and/or venue in which the art imagery is to bedisplayed. To accomplish this goal, the Loupe system creates artchannels and art imagery is assigned to those art channels based upon abalance of traditional art world categories, such as art channelsdedicated to Abstract, Black & White Photography, Human Form, and othertraditional categorizations; special collections from galleries andother art world entities; philosophical and experiential themesexpressed in artists' statements; and lifestyle categories such as HappyHour, Tranquil, Seasons, and other categories that may resonate withusers seeking visual art experiences to complement their daily life.

In an embodiment, other channels are more automatically assigned byLoupe system processes, which may include Stream by Color and Stream byArtist categorizations. In an non-limiting example, the Stream by Colorchannels may include art imagery that is, to the eye of the Loupecuration team, compatible with a color stream even if the particular artimage does not qualify for a particular Stream by Color channel basedupon an analysis of the image or the percentage of color in the image.Artwork imagery may also be assigned to a specific channel based uponanalytical data provided by one or more user behavior data insightservices. These user behavior data insight services may inform the Loupesystem which channels of artwork, types of artwork, and specific artworkimages are popular with subscribers, venue owners, and other users ofthe Loupe system across all delivery platforms.

In an embodiment, continuing to update the offerings of art imageryacross the Loupe system channels occasionally necessitates the removalor reassignment of art imagery from the system channels. The Loupesystem continually evaluates which art channels are viewed mostfrequently, the duration of viewership, and which individual art imagesexperience the longest view times in terms of user pausing on an artimage, which individual art images are marked as favorites, and whichart images are purchased most frequently as physical prints or originalsby users of the Loupe system. In this manner, the Loupe system maycontinually elevate the standard of art imagery offered by the system asthe art imagery is experienced by greater numbers of users.

In an embodiment, attribute properties are created and grouped utilizingthe metadata associated with the art imagery. In a non-limiting example,the metadata used by the Loupe system may be primarily curated using theSpree content input system, although alternative content input systemsmay be utilized without departing from the scope of the innovativesystem. Such characteristics as artists, channels, medium, price,geography, marketplace availability, and moods are taxonomies assignedto the artwork imagery by the Loupe curation staff. Other metadata maybe generated via algorithms implemented in the Loupe system. In anon-limiting example, the attribute “Recently added” is a verysimplistic attribute in which all newly added artwork, within apredetermined time period and/or time frame such as “within the pastthree months”, is taxoned as recently added. Subsequently, that taxon isremoved once an artwork image has been in the system for a time periodexceeding the predetermined time period or time frame.

In an embodiment, the Loupe system may use a random series of imagesthat are taxoned based upon the channel that the images to which the artimage may belong. The defined channels and the content of these channelsmay be curated by the Loupe curation team for relevance and consistency.The art images may be generated for inclusion to specified channelsthrough the identification of the Loupe curation team. Additionally,however, art images may be recommended for custom streams of contentbased upon the understanding by the Loupe system of a particular user orvenue's past preferences and past behaviors. Thus, art image content maybe generated for a standard channel or for a custom, specified channeleither at the direction of the Loupe curation team or through thespecification by the Loupe system based upon past user or venue history.In an embodiment, the Loupe system may also permit a user to set aspecific sequence to the next piece of content, a particular art image,that may be repeated each time the channel is launched. This capabilitypermits the selected channel to intersperse branded content into thestream of art imagery at a specific frequency. In a non-limitingexample, the Loupe curation team relies upon the same criteria used toinput and onboard art imagery to the platform and to an individualpre-selected channel. The permits increased scrutiny on the art imageryto be included for presentation on a pre-selected channel. Thisincreased scrutiny ensures that the direct juxtaposition of art imageryto be displayed in a sequence following a particular composition, form,color, and aesthetic that may be pleasing to consumers of thepre-selected channel.

In an embodiment, color taxoning is a much more complex process. TheLoupe system utilizes uses a set of algorithms to pull the top, definedas the dominant, colors from an artwork piece by the color hex values,where each hex value represents a particular color on a color wheelchart. The system may then calculate a “distance” those colors are frompre-determined table of color values that represent a specific “color”category. There is a “cliff” that an artwork piece must reach to bedeemed close enough to those colors to be considered, using distancefiltering based on our lookup table. In a non-limiting example, if anartwork's color profile distance is too far from the predeterminedpalette colors, it isn't considered a member of that taxon for theartwork piece being classified. The Loupe system provides the ability tocollect both latent and active real-time data. Latent data collected mayconsist of meta-data surrounding usage data when one or more users areinteracting with the visual imagery displayed, such as user pause,replay, delete, or other functions at the direction of a user, andactive content consumption data may consist of user selection, inputdata that defines preferences, and selection of content to purchase artobjects presented. The latent and active real-time data collectionprovides an ability for the system to analyze the collection of datafrom each user to learn more about the user in terms of interaction withthe user interface, and also know about what content the user hasconsumed.

The Loupe system may have the ability to know about content consumed byeach user as well as emotive responses to the content by otherprofessional crowd-sourcing communities that specialize in this sort oftask for reliable and trustworthy assignment of attributes. In anon-limiting example, the crowd-sourcing identification may be performedby a company such as Figure-Eight (www.figure-eight.com). This processis commonly referred to as tagging and labelling of data. The contentconsumption data and operation rules permit the Loupe system to avoidrepetition within a defined period, whether that period is defined as asession, a particular time period, or even a particular location that isassociated with a session or time period. It is critical to be able tocollect data around the content already viewed by a user within adefined period and/or location so that the content and sequence ofcontent is not sent twice to the user during the defined period and/orlocation.

The Loupe system breaks down the essence of a visual art piece into aseries of attributes that characterize the way that the art piece iscreated (as in tools and methodologies of artwork creation), and how itis perceived in digital form, as well as, the sensitivities andpotentially perceived emotions from the perspective of the consumer.

Characterizing visual art piece images is built upon training a neuralnetwork through the use of a training dataset to identify images basedupon an attribute taxonomy. The attributes of the images presented inthe training dataset train the neural network to define and createclassifications for the images. In the aggregate, the system will learnto classify images based upon the patterns present in the images and thecurator definitions recorded for each image. The learner system wouldcreate a set of attributes, once again based upon definitions suppliedby human curators, and ascribe the weighting of each attribute for eachimage. As additional training images are presented to the learner systemneural network, given a set of beginning seed weight values, weightingfactors are increased or decreased to arrive at a proper attributeweighting to place each image into an accurate image classification. Theneural network thus creates the weighting of attributes that inform theplacement of each image into a particular classification. This trainingdataset provides the system with verifiable examples of what imagesbelong in a particular classification and what images do not belong in aparticular classification.

At the completion of the training phase, the learner system will providea classification for new images presented to the system from a testimage dataset. The test image dataset is composed of images that haveknown classifications, but are unknown to the learner system. This testimage dataset is used to validate the fact that the learner system hassuccessfully learned from the training image dataset how to classifyunknown images.

The characterization of the art elements, through the attributetaxonomy, provide means to group items that have similarities, andtherefore provide a sequence delivered as a stream of representationalexamples of imagery that is delightful, pleasant and natural to thesenses of the consumer and subject to the location of the consumerwhether in privacy of home or in public setting, and in which kind ofpublic setting. A side effect of the sequencing is to provide anon-intrusive discoverability mechanism that can be used to sell art.

The combination of a unique and proprietary set of properties orattributes that define a piece of artwork within the Loupe system isdefined as Visual Art DNA.

In an embodiment, the Visual Art DNA of art images as created by theLoupe System is a combination of attributes that when combined describethe essence of the art thus represented. This essence can be humandefined, automatically defined through a machine learning capability, ora combination of both human defined and automatically defined. Theconstructs that describe the essence may evolve over time in how theyare defined and how they are generated.

The attributes defined for use in the Loupe system in the categorizationof art are used, both together and independently, to uniquely describeeach artwork's fundamental visual perception. The Loupe system utilizesa variety of algorithms to train machine learning models. These trainedmodels may then be used to automate the value assignment of eachattribute for an artwork. This value assignment has the effect ofpredicting the Visual Art DNA of a given artwork without the need forhuman intervention once the models are trained. The Visual Art DNAdiscovered for each art piece may then act as a reference for aparticular human viewer's affinity for any subset of attributesdiscovered for that art piece. Utilizing a subset of the attributes theLoupe system may then uniquely weigh the importance of each attributewhen scoring the overall affinity a human viewer may have for aparticular piece of artwork.

The second portion of the Loupe system, comprises the publishing portionof the system. This portion of the system controls content managementproviding all of the tools to manage individual assets, populate VisualDNA attributes, such as tags, managing publishing dates, times, andcategorization, and making assets public and available to users throughthe Loupe publishing capability. The publishing portion of the systemutilizes the Visual Art DNA process to describe the digitally perceivedessence of an art piece. The Loupe system will also have categories forthe sake of manually curating groups of art pieces. The categories,however, will be driven as much from an experience perspective, ratherthan solely intellectual, academic and historical components of an artpiece. The Loupe system will present a combination of categories andattributes definitions. As a user experiences art works delivered totheir attention through a Loupe art player, the Loupe system gatherslatent and active data from the user to form the basis for processing bya recommendations engine to provide users with art works they may wishto view, combine into a playlist, or acquire. The recommendations enginewill be based on learner systems that will group images based on theirfundamental characteristics, as well as incorporating the user's tasteand preferences.

A favoriting system may be created that utilizes specific user historydata to create a list of favorites for each specific user. The userhistory data, composed of what images a user has navigated to, captured,selected, and/or purchased, informs the user image preferences basedupon the classifications established by the learner system during thetraining phase.

In an embodiment, the favoriting system begins with the identificationof each user as the user logs into the Loupe system. As each userselects artwork pieces and designates the selected artwork pieces asfavorites, the Loupe system adds the selected artwork pieces to a mapwhich assigns favorited artwork to each user. The Loupe system can thencreate and present to a user a channel of only their favorites by usingthis map.

The mapping is done using a database table which lists a user_id andproduct_id so that they are able to be retrieved together.

The third part of the system comprises all interaction between a userand the publishing system implemented within the Loupe system to formthe Loupe Streaming runtime portion of the system. In this portion ofthe system, utilizing categories and other information from thepublishing portion of the system to feed the Loupe Streaming runtime.The Loupe Streaming runtime consumes functionality from a recommendersub-system to provide a sticky personalized experience.

The Loupe Streaming runtime portion of the system collects data fromeach user to know what presented content each user liked and didn't likeand to what degree it was liked, and other types of active userinteraction with the presented content over a period of a session, day,week or other time duration. Feedback from each user on likes anddislikes with regard to the art presented may enhance the predictivemodels for what art to present based upon the stated preferences and thelatent and active data collected from each user. It is critical to theperformance of the predictive model and the performance of the Loupesystem to understand what content each user has liked and what contenteach user has not liked.

The Loupe system may define one or more standard or starter userprofiles where these standard or starter user profiles may beprepopulated with one or more initial attributes collected from the userduring signup with the Loupe system. In order to prevent a “cold start,”which would prevent the efficient operation of the predictive modelsutilized within the Loupe system, it is essential to collect somepreliminary data from the user when the user signs up. This initial datacollection provides some initial data upon which the predictive modelsmay function.

The art images selected for presentation to a user may be presented as aseries of still images that are properly formatted for each displayscreen that the user has designated as a destination display for theimages to be presented. The Loupe Art Player may slowly pan eachdelivered image across the display on which the image(s) are presented.In a non-limiting example, the speed of the pan depends upon how closelythe aspect ratio of each art image matches that of the source device orscreen display. The delivered images are presented as a stream of stillimages so as to provide the user with the ability to select any of thedelivered images for later purchase through an integrated electronicmarketplace. Additionally, the Loupe Art Player may stream images otherthan still images, such as motion art as display in formats such as, ina non-limiting example, animated art presented in motion capable GIF(Graphic Interchange Format) images.

In an embodiment, the Loupe system contains a Loupe Web Streaming Playeras well as other device specific apps such as but not limited to AppleTV app, Amazon Fire app, an Android app, etc. The Web Streaming playeris implemented such that the content may be transmitted to and played ona screen associated with any network capable device. This implementationprovides the content from the Loupe Web Streaming Player for display onlarge format screens, independent displays, computer displays, videowalls, projected digital murals, mobile device displays, web capabledisplays, smart phone displays, tablets, and even smart watch displaysand via Content Management Systems (CMS) software to control multipledevices simultaneously. Smart watch displays and any products developedby digital display and digital signage companies—or any such displaycapable products. In an embodiment, the Loupe system also contains aLoupe player providing the capability to present content to a variety ofproprietary LED displays, such as but not limited to displaysimplemented for all screen formats for mobile devices and other LEDdisplays.

The Loupe system presents a user with access to an electronicmarketplace where a user may purchase fine art reproductions andoriginals of any art images presented to the user through the Loupe ArtPlayer. The electronic marketplace will be enabled to capture theidentification of the art image or image(s) in which the user hasexpressed interest, providing the user with options for format, size,and delivery of the purchased art image(s) as a fine art print or inselect cases the original artwork itself in real time.

The Loupe system provides delivery of content that explores thepotential and fascinating interplay of music and art attributes of thedisplayed content. The Loupe Art Player, in any configuration, isconducive to use with music players, such as, in non-limiting examples,Spotify, Apple Music, and Pandora.

The Loupe system also provides the capability for private channels to becreated and provisioned when a user acquires a commercial license. TheLoupe system can send a unique stream of art to a business location thathas paid for a license, such as hotels, airports, sports venues,conference centers, casinos, restaurants, business lobbies and commonareas, and other commercial spaces. The offering by the Loupe system isan art programming service provided to those commercial users wishing toprovide visual art displays to large groups of individuals utilizing thecommercial space. The private channel option permits the Loupe system toprovide—and continually refresh—a collection of art that speaks tocommercial user's unique art identity.

The Loupe system allows for an integrated marketplace for each imageprovided by the system to those who may have seen an art image displayedin a public place, such as a commercial space or business. In theintegrated marketplace, a user may log into the Loupe system andinteract with each image to purchase, save for purchase, and otherwiseinteract with imagery selected by the user. The imagery interaction inthe integrated marketplace managed by the Loupe system allows for anintegrated marketplace in real time.

The fourth portion of the Loupe system provides a framework forpredictive modeling utilizing machine learning. Recommendation enginesare typically powered by predictive models, composed by items having oneor more attribute definitions, collaborative filtering and machinelearning algorithms. It is essential to have a framework to run themodels as well as test the models before putting them in production. Thesystem may have the ability to run the models in batch fashion, but mayalso be able to filter the results in real-time by utilizing a user'sreal-time behavior.

In an embodiment, before recommendations can be executed and provided toa user, metadata for the content must be provided or created so thatproper content can be selected as one or more recommendations for auser. For recommendations to be effective, they can't be selected as a100% result driven from machine decisions. Effective recommendationsrequire some sort of human interaction to define the characteristics ofthe content. Attribute definition for the content database is the firststep in discovering and creating metadata to be associated with eachitem of content. Initial categories for the attributes to be definedinclude:

-   -   1. Medium Fundamentals. Depending on the content type (i.e.:        photo, painting, video), the medium fundamentals will be        different, but these are fundamental characteristics that        differentiate, for example, the content type, (i.e.: light,        motion, etc.) from all other representations of that content        type. Specialists in each specific media may provide input as to        the list of attributes for each content type.    -   2. Composition and Context. This is related to the composition        and context, in other words, a Studio photo, Outdoors photo,        Landscape photo, or other location or background will assist in        defining the attributes of composition and context.    -   3. Lifestyle/Activity. This is how the piece of art will provide        an optimum experiential atmosphere to complement the viewer's        activity, i.e. child's birthday party, rock concert venue,        romantic date, elegant dinner party, edgy downtown party, style        of music being consumed, etc.    -   4. Time Period and Geolocation. This is how the Loupe system may        determine time and physical location of both the Loupe artist        and the Loupe viewer/user.    -   5. Subject/Content. This is how the Loupe system determines the        primary content of the art image and may feature portrait, still        life, nature, animals, nudes, landscape, abstract, people        (multiple), cultural appropriation, architecture, sports,        transportation, music instruments, outer space, food, fashion,        children, and other subjects to be defined.    -   6. Medium. This is how the Loupe system determines the materials        used to capture the image and may include oil painting, acrylic        painting, photo, illustration, pencil, watercolor, video, mixed        media, animation, digital art, cinemagraph, sculpture,        assemblage, collage, encaustic, etching, screen printing,        intaglio, gouache, mural, and/or installation.    -   7. Art Movement Inspiration. This attribute determines how the        Loupe system captures the era or movement for each art image and        may include Renaissance, Baroque, Rococo, Romanticism, Realism,        Impressionism, Fauvism, Expressionism, Cubism, Futurism,        Abstract Expressionism, Dada, Surrealism, Art Deco, Pop Art,        Conceptual Art, Land Art, Minimalism, Light and Space, Neo-Dada,        and Feminist.    -   8. Art Style. This attribute determines how the Loupe system        captures the style of each art image and may include Realism,        Representational, Impressionism, Street Art, Modern, Fine Art,        Dada, Urban, Abstract, Documentary, Surrealism, Nonobjective,        and Narrative (tells story).    -   9. Art Criticism or Artist Career. This attribute determines the        career progression of an Artist and may include Emerging,        Mid-career, Represented, Blue Chip, Established, Masterpiece,        Avant-garde, and Iconic.    -   10. Mood. This attribute determines how the Loupe system may        capture the mood of an art image and include Optimistic,        Inspiring, Relaxing, Energizing, Party, Melancholy, Dark, Angry,        Sexy, Shocking, Humor, Dramatic, Romantic, Happy, Sad,        Frightening, and Introspective.    -   11. Subject Interpretation. This attribute determines how the        Loupe system determines an interpreted categorization of the        elements in an art image and may include Death, Love,        Environmental Activism, Feminist, Family, Friendship, and        Futuristic.    -   12. Music Genre Pairing. This attribute determines a musical        style that is most associated with the art image being displayed        and may include Classical, Jazz, Electronic Dance, R&B,        Ethnic/World, Blues, Rap, Rock Country, Music Theater/Opera (no        subgenres).    -   13. User Activity. This attribute expresses the action or        activity expressed within the art image and may include Party,        Workplace, Relaxation, Romance, Exercise, Medical/Health, Music        Listening, Waiting Areas, Drug action.    -   14. Geo-Location: Art Subject. This attribute expresses the        geographical locale expressed within an art image and may        include Asia, North America, South America, Europe Caribbean,        Africa and other geographic locations.    -   15. Geo-Location: Artist. This attribute expresses the        geographical locale for the artist who created an art image and        may include Asia, North America, South America, Europe        Caribbean, Africa and other geographic locations.    -   16. Physical Environment. This attribute expresses the physical        milieu captured within an art image and may include Parks,        Forest, Countryside, Ocean, City, Mountains, outer Space, and        Ambiguous Space.    -   17. Location Type. This attribute expresses whether the art        image is set in an Interior or an Exterior space.    -   18. Time of Day. This attribute expresses the portion of the day        in which the art image is set and may include Sunset, Sunrise,        Night, Daylight or other defined dayparts.    -   19. Seasons. This attribute expresses whether the art image is        set in Spring, Fall, Summer, or Winter.    -   20. Color/Hue. This attribute expresses the main color into        which the art image is to be categorized and may include Blue,        Red, Yellow, Green, Brown, black, White, Teal, Pink, Orange,        Grey, Purple or other defined base colors.    -   21. Palette. This attribute expresses the primary group of        colors into which the art image is to be categorized and may        include Earthy, Rustic, Neon, Black and White, Sepia tone,        Neutral, Subtle, Monochromatic, Metallic, Bold, Industrial,        Pastels, Warm Tones, Cool Tones or other defined color        groupings.    -   22. Color Scheme. This attribute expresses the overall color        composition and may consist of Tertiary, Complementary,        Analogous Colors, as well as other defined color schemes.    -   23. Perceived Price. This attribute expresses a dollar value for        the art image.    -   24. Orientation. This attribute expresses how the art image is        best viewed and may consist of Portrait, Landscape, or Square        values.    -   25. Light. This attribute expresses whether the lighting in the        art image is Darker or Brighter, harder or softer and may be        rated on a sliding relative lighting scale.    -   26. Temperature. This attribute expresses the subjective feeling        of warmth within an art image and may be expressed on a sliding        scale of Warm (yellow/red/sun/fire) versus Cool (blue/Snow/Icy),        or Saturation.    -   27. Motion/Movement. This attribute expresses the activity        within an art image and may be defined as High Activity, Static,        Repetition or other defined activity values.    -   28. Rhythm. This attribute expresses whether the art image        contains Repeating Objects versus Singular objects.    -   29. Texture. This attribute expresses the perceived texture of        the art image and may consist of Smooth, Raw, Rough, Liquid,        Grainy, Reflective, Painterly or other defined textures.    -   30. Mass/Form. This attribute expresses whether the art image        contains large objects or small objects.    -   31. Focus/Depth of Field. This attribute expresses the visual        sharpness of the art image and may be represented as Blurry,        In-Focus, Partial Focus, Foreground, or Background.    -   32. Predominant Space. This attribute expresses whether the art        image is represented as a positive or negative space.    -   33. Compositional Arrangement. This attribute expresses how the        elements of the art image are arranged within the image and may        be represented as Balanced, Unity, Asymmetrical, Diptych,        Triptych, and/or Repetition.

In an embodiment, once categories of attributes have been established,the process for defining attributes of the content is created. One ofthe characteristics of the product is that content will be humanlycurated by visual artists and related professionals as well as by powerusers of the platform. Part of the curation process is also defining theattributes for all content elements to create an attribute taxonomy. Theemotion category is subjective, so the following steps should befollowed for the emotion category:

-   -   Capture the profile/tastes of each one of the curators, using        the same attributes as users    -   For every element, get the emotion from each one of the curators    -   Persist the emotions categorized by profile type.

Human curators and/or professional crowd sourcing may be used to createthe training set of images that populate the training image dataset. Asmall set of images may be provided to a set of human curators, usingthe defined attribute taxonomy for classification, to place the imagesin the appropriate classifications. These human curated classificationsare associated with the images as metadata and the training imagedataset is created.

Once attributes are available to the Loupe predictive modeling module,the predictive models and filters should be able to decipher the nextpiece of content that the user will enjoy. Machine learning, forexample, provides tools and techniques such as Clustering, CollaborativeFiltering, Matrix Fac, and Neural Nets, that can be applied to defineand establish the relative weighting needed for the neighborhood models.

The following techniques may be used to implement the models as part ofthe Machine Learning capability:

-   -   Clustering. This technique is used for machine generated        clusters of items. These will change automatically over time,        and new clusters (segments) may arise.    -   Collaborative Filtering. This technique is used to find        similarities between items and between people.    -   Neighborhood Models. It's a type of model used for the        filtering. These models use a weighted average to predict the        ratings of an item, based on previous ratings.    -   Matrix Factorization. This is used for latent factors,        effectively an optimization.    -   Neural Networks. Neural Network techniques can be applied for        the machine learning aspects.        Upon collection and categorization of all incoming data, the        models will run periodically and the end result is:    -   Clusters of items    -   Based on similarities, come up with a table of item        classification for items that the user may want to see. This        table can be persisted in ES by user.    -   The weights for the collaborative filtering.

In an embodiment, when the “next piece of content” is requested, thecontent is fetched based on the model results. There is, however, a setof filtering rules that kick in to make sure that content is notrepeated, and also keeps into consideration the user's latest ratings.In fostering the analysis and filtering for retrieving the “next pieceof content” the user must rate content so that the rating become inputfor the learning algorithms. One of the challenges is to expose theratings capabilities in a non-intrusive way that also gives the user anincentive to provide them. Basic requirements to implement the Loupe webapplication may include:

-   -   A Device that is HTML5+JavaScript compliant, and supports        hardware accelerated rendering    -   The Loupe system relies on CSS transformation animations of        still imagery and relies on the proprietary Loupe web player as        well as artwork specifically submitted to Loupe as motion art,        with the art player's capability to also stream any channel as        “fixed” video files.    -   The Loupe system Web Streaming Player provides support for all        screen aspect ratios.    -   The Loupe display capability is mobile responsive—While there is        no minimum screen size, the web application will scale its UI        down as it approaches 768 px wide Primarily noticeable on the        home screen whereby the number of channels across per row        decreases as screen size is reduced, down to one column of        single channels visible on an iPhone screen.    -   In a non-limiting example, the dimensions for a static image to        be presented on a display associated with a smart TV device,        which has a 16×9 fixed aspect ratio, is approximately 1800×1013        pixels.

Turning now to FIG. 1, this figure presents a view of the Loupe systemarchitecture consistent with certain embodiments of the presentinvention. In an exemplary embodiment, the diagram presents a view ofthe high-level architecture components. The approach is to go withtraditional services architecture, along with key value stores for longterm storage and for overall data persistence.

In an embodiment, the following is the description of potentialcomponents that may comprise the Loupe system. However, this set ofpotential components may in no way be considered limiting as additionalor substitute components may be utilized in place of any component inthis description. The Loupe system may have an Event Service 100 that isresponsible for collecting events in real-time fashion from thefront-end application(s). The Event Service 100 continuously collectsevents related to user behavior and content consumption as an active,dynamic data capture service.

The Event Service 100 may transmit captured data to an open source, highthroughput message bus with a publisher consumer model that supports nnumber of producers and n number of consumers 102. In a non-limitingexample, the Kafka service is one such service that may be incorporatedwithin the Loupe system, however, this should in no way be consideredlimiting as other services may provide the same capability withoutimpacting the innovation described herein. This high throughput messagebus 102 will be used to pass events around throughout multiple services.At the beginning, the number of producers and consumers will be ratherlimited, but the producers and consumers will continue to increase asthe Loupe system continues to operate.

The open source, high throughput message bus 102 may transmit data toanother open source system, used for batch analysis of data, and alsodata stream processing at 104. This batch analysis and data streamingcapability 104 may be implemented by incorporating the Spark open-sourcesystem in a non-limiting example. The real-time data analysis may beperformed by the Spark system, as well as the operation of thepredictive models utilized within the Loupe system. In a non-limitingembodiment, the Spark open-source system includes MLib which containsimplementations of various machine learning algorithms. The Spark systemmay also come with a graph analysis library that may be implemented infuture releases of the Loupe system.

Data captured by the Event Service 100 may be stored within a data filestorage system 106, such as the S3 data store released by Amazon WebServices (AWS). The data file storage system 106 may be used for longterm archival and storage of collected raw data.

Additionally, both raw data and data about users may be stored in anelectronic data file storage system known as the Account Store 108. TheAccount Store may hold metadata about user that includes userconfiguration information and preferences information in terms ofattributes representing the user's taste in art imagery.

The Loupe system may provide an Elastic Search component 110 implementedas a service that may be used to index user and content data for thesake of scalable and low latency search for desired art imagery.Additionally, the Elastic Search may store user recommendations andpreferences, as well as the content metadata and attributes as receivedfrom the Spark component 104. The Elastic Search 110 data store permitsall collected data associated with one or more users or venues to bereadily searchable and be retrieved in a low latency fashion. TheElastic Search 110 may store the results from running the predictivemodel, so at any point in time the Elastic Search 110 can returnrecommended lists of art imagery viewed for a given user.

The Loupe system provides a Filter Rule Service 112 to filter artimagery based on the most recent actions of a user which may not havebeen considered on the most recent run of the predictive models. Innormal operation, the predictive models are run once a day to update alluser parameters and input the most recent data for each user. Additionalfactors that may affect the filtering rules in future updates of theLoupe system may include special event for a selected genre or otherevents configured for the Filter Rule Service 112. The Filter RuleService 112 may receive real time events from the Kafka service 102 toprovide for real time updates of the user's profile.

The Loupe system provides art imagery streamed to users and venuesthrough a Delivery Service 114. The Delivery Service 114 connects to aclient application that is initiated and used by a user or venue todeliver instructions on what art image content should be streamed to theuser or venue and in what order. As each art image is displayed, theDelivery Service 114 provides instructions to the application on whatart image should be displayed next.

The Loupe system staff interacts with a Content Service 116 to ingestcontent, define the content attributes, create genres, classify thecontent based on genres, and create attribute categories. The ContentStore 118 is the electronic data store that receives definition,classification, and attribute selection metadata from the ContentService 116 to provide the art imagery to users and venues.

In an embodiment, the Content Service 116 and Delivery Service 114combine to select, prepare, and deliver art content that is specific toa user based upon latent and active content selection and viewing by theuser, or requested by a user in a defined playlist.

Turning now to FIG. 2, this figure presents a view of the Loupe systemVisual Art DNA content identification process consistent with certainembodiments of the present invention. In an exemplary embodiment, thesystem presents a process for creating Visual Art DNA as an identifierfor curated, customized and personalized for a user. The process beginsat 200 by interacting with a user to first select one or more visual artrepresentations that are preferred by the user. The art work(s) selectedare analyzed for visual perception 202 and awareness of the visualcomponents of the art work(s), information about the art work(s) fromacademia, historical records, and artistic techniques 204, and emotionand/or intention established for the art work(s) 206. The emotion orintention aspect of the art work(s) 206 may be crowd sourced frommultiple human sources.

The Loupe system then synthesizes the essence of each art work at 208and decomposes the essence through a custom algorithm into multiplecategories of attributes, creating attribute lists for each artwork. Theart work attributes are encoded into a machine-readable format toprovide for indexing in a computerized system. The computerized systemencodes the art work for classification, co-relation to other artwork(s), and creates an index for each art work. The Loupe system alsoincorporates valuation and information from human curators to provideboth computerized and human information to a recommendations engine thatmay then select and present art work(s) 210 to a user based upon thecreated Visual DNA for each art work, where the Visual DNA provides forthe selection of art work(s) that are more strongly associated with thepreferences and interests of the user.

Turning now to FIG. 3, this figure presents a view of the Loupe systemplayer control consistent with certain embodiments of the presentinvention. In an exemplary embodiment, a Loupe system player willprovide a continuous and full screen immersive stream of still images ofart or motion art (i.e. computer-generated animations, video art,etc.)—to a user based upon the discovered recommendations for a user.The Loupe system utilizes both machine learning and ArtificialIntelligence (AI) techniques to enhance the decision of what art imagesto present to a user in combination with visual perception, academic andhistorical aspects, and the discovery of human curated emotion andintention aspects of art images.

As before the process begins at 200 by interacting with a user to firstselect one or more visual art representations that are preferred by theuser. The art work(s) selected are analyzed for visual perception 202and awareness of the visual components of the art work(s), informationabout the art work(s) from academia, historical records, and artistictechniques 204, and emotion and/or intention established for the artwork(s) 206. The emotion or intention aspect of the art work(s) 206 maybe crowd sourced from multiple human sources. The system may then createa system of shared attributes 300 by discovering unique attributes ofart imagery within the data store of art imagery that may combine withmultiple attribute recommendations from the Art World, Neuroscience, andcrowd sourcing. The machine learning algorithms utilized by the Loupesystem may combine all sets of art imagery attributes to create a robustart image stream.

This combination of techniques and attributes provides a uniquecontinuous and uninterrupted streamed presentation of art images thatare more precisely tailored to a user's concise preferences in artimagery 302. Upon presentation, the Loupe system may maintain a log ofimagery presented on a per user basis to more efficiently integrate withan integrated electronic marketplace where a user may purchase an artimage(s) desired.

Turning now to FIG. 4, this figure presents a view of the Loupe systemhome page consistent with certain embodiments of the present invention.In an exemplary embodiment, the Loupe system home page presents a userwith a selection of images and quality of imagery that may be accessedin the Loupe experience. At 400, these cover images displayed representthe theme and content direction of the curated collection within thatchannel and are regularly updated to remind the user that the channelcontent itself is regularly updated. The Loupe system home page providesthe user with access to a menu of options or customization of thestreams, whether filters to remove certain imagery or alternate homescreens that display the Loupe art catalogue in a different type ofconfiguration of channels, i.e. to stream by color, to stream by artistetc., to provide access to the electronic marketplace powered by theLoupe system, a login option for members and returning users who haveregistered with the site, and information about Loupe and the servicesprovided.

Turning now to FIG. 5, this figure presents a view of the Loupe systemcontent presentation consistent with certain embodiments of the presentinvention. This figure presents at 500 a non-limiting example of avisual art image that could be selected and presented to a user. Thevisual art image may be a transformation animation of a still image, ananimated GIF, an image captured from a video stream, or any art displayimage with which the Loupe system may provide an interaction capabilitywithin the real time marketplace. The image is formatted for the displayidentified by a user for delivery of the imagery. Although not shown,the image presented to a user may pan from left to right and/or top tobottom of a display screen so as to present the art image in the properquality and resolution.

Turning now to FIG. 6, this figure presents a view of a Stream by Coloroperation consistent with certain embodiments of the present invention.In this embodiment, the Loupe system Art Player utilizes an algorithmthat normalizes the dominant color value in an image 600, thencalculates the normalized color's distance from a custom table of colorvalues to ultimately map the normalized color to the closest color“bucket” within a pre-defined color range. The distance filtering isbased upon a custom color lookup table established in associated withthe algorithm utilized.

In an embodiment, the Loupe system calculates the distance from allcolors defined in a palette 602 and categorizes each color as belongingwithin a particular color palette if the primary color perceived iswithin a score of 0.5 of that primary color 604. Colors with a scoregreater than 0.5 away from the primary color of that particular paletteare not included within the particular palette as being “too far” awayfrom the primary color of the particular palette 608 and are notincluded in the art image data stream being presented. The system maythen add the human eye perception/curation to the categorization processto confirm that an art image to be classified feels like the art imagebelongs in a particular color stream 606. This confirmation creates acohesive and logical progression of experience and mood as art imagesare presented to a user or within a venue 608. After including ordiscarding a selected art image from the art image stream, the next artimage is selected at 610 and similarly evaluated for inclusion ordeselection.

The Stream by Color operation takes advantage of a set of customizedalgorithms and identifying taxons to permit the selection of images tobe presented to each user based upon his/her preferences as captured andanalyzed by the Loupe system. The operation may utilize informationconcerning the emotion or intent of art images in the curation process,however, the images presented may be selected automatically withoutfurther human curation. This operation creates an effective stream bycolor experience that is a fluid representation of color associated artpieces presented to the user.

Turning now to FIG. 7, this figure presents a view of the Loupe systemuser behavior verification consistent with certain embodiments of thepresent invention. In this embodiment, the user interaction with theLoupe system through the use of Visual Art DNA is maximized by theunderstanding of emotional and stress responses in humans. Understandingthese factors and incorporating the factors that increase relaxationinto the recommendations engine of the Loupe system, also increases theamount of time the average user continues to stream art images selectedthrough the use of Visual Art DNA and displayed via the Loupe ArtPlayer. Reducing stress and increasing the feeling of relaxation in auser leads to an average viewing time, or “stickiness”, of over 2.5hours per viewing session. In an embodiment, the Loupe system maycollect the number of Daily Active Users (DAU) 700 and the number ofMonthly Active Users (MAU) 702. The determination of “stickiness” withregard to an art image viewing experience may be calculated by dividingthe number of DAU by the number of MAU to arrive at a percentage valuefor the stickiness parameter. The stickiness value may be presented as:S=DAU/MAU and is expressed as a percentage 704. A higher percentagevalue equates to a higher number of users returning to the Loupe systemstreaming view of art imagery. Thus, in this non-limiting example, thehigher the percentage value, the greater the stickiness value for aparticular stream of art imagery. The closer the DAU value is to the MAUvalue, the greater the stickiness or engagement value is for the Loupesystem art imagery stream. This dynamically calculated higher stickinessvalue (S) also may equate to a higher return frequency value for MAUswho are using the Loupe system viewer application to view one or moreart imagery streams or channels. Each channel has an S_(avg) stickinessvalue for the current parameter settings for the particular channel. TheLoupe system may analyze stickiness by determining for which sets ofattributes for a particular channel that S>S_(avg) is true 706.

The Loupe system may interrogate the attributes of channels that havethe greatest or highest stickiness parameter values and utilize theseattributes to increase the stickiness value for other channels byreplicating those high stickiness parameter attributes to the otherchannels. This replication is performed by replacing the parameterattributes for a channel whenever the calculated dynamic S>S_(avg) for aparticular channel 708 is true. If the S>S_(avg) determination is false,the parameter attributes for a particular channel are retained asunchanged 710.

The recommendation engine is optimized to increase viewing time throughmultiple factors herein described and continues to optimize theexperience and increase the “stickiness” of the presented art imagery asthe Loupe system gathers more interaction data from a user over time712.

Turning now to FIG. 8, this figure presents a view of the streamingselection menu of the Loupe system consistent with certain embodimentsof the present invention. In an embodiment, in the Loupe systemstreaming is defined as shuffling all presented images to an alternativeview of the images. This shuffling rearranges all content presented to auser to a different set of categories by which art images are selectedfor streaming presentation by the Loupe system viewer function. A usermay select any of a group of categories that will govern the selectionof streamed art images. In non-limiting examples images may be selectedaccording to principle color, by artist, by channel, according togeographic location of the user, geographic location for the creation ofthe art, or any other pre-defined category. The categories may bepresented to the user for user selection and the Loupe system streamingfunction may then use the selected category as the guiding parameter forthe classification and selection of art images that are then displayedand streamed to the user.

In an embodiment, the user may be presented with a selection menu 800.Within the selection menu 800 the user may initiate the streamingfunction through the selection of the Stream By menu tab 802. Uponselection of the Stream By menu tab, the various stream by categoriesthat have been prepopulated on the selection menu 800 will be active,meaning that the user may select any of the categories as the stream bycategory for use by the Loupe system streaming function for theselection of art imagery that is then displayed as a stream of artimages to the user. In non-limiting examples, a user may select aChannel 804 category and upon selection the Loupe system will select artimagery associated with a particular channel of art works and presentthese as a stream of art images to a user. Alternatively, a user mayselect a Color 806 category to activate a Stream by Color selection tochoose art images that have a particular color or colors that aredominant in the art image and present these as a stream of selected artimages to the user. In another nonlimiting example, the user may selectan Artist 808 category to select art images by a particular artist andstream the selected art images to the user. Additional categories may bepresented to the user, as previously described, which will be presentedto the user for their selection and that will govern the choice of artimages to be streamed in a display to a user based upon the selectedcategory.

Turning now to FIG. 9 is a view of a selected streaming displaypresented to a user consistent with certain embodiments of the presentinvention. In an embodiment, when a user selects a Stream by categorythe user is then presented with an alternate home screen upon which artimages in keeping with the category selected will be displayed as astream of art images consistent with the selected category. In anon-limiting example, upon selection of the Stream By Color streamingcategory the user may be presented with an alternate home screen withimagery arranged by the predominant color of the image 900. In thisdisplay the user is presented with shades of a particular color. In anon-limiting example, the user may be presented with imagery that allhave the predominant color of “red” as the color of choice for aparticular stream of art imagery. When the user selects a particularswatch on the screen, the Loupe system Streaming Function will presentthe user with the name of the color for that particular selected swatch.The user may also be given the option to select different colors, or maysimply review the stream of displayed art imagery as the Loupe systemStreaming Function presents different color pages for review. The userselection of the color in which they have interest may be utilized bythe Loupe system to reinforce color selection in the art imagery that isselected to be streamed to the user.

Turning now to FIG. 10, this figure presents a view of the Loupe systempause and purchase capability consistent with certain embodiments of thepresent invention. Users of the Loupe system are presented with astreamed set of selected art imagery. At 1000, the Loupe system presentseach user with the ability to click into the stream and pause it to makea purchase through the integrated marketplace. At 1002, the user ispresented with a BUY button prominently displayed outside of the imagedisplay are that permits the user to click on the BUY button, once thestream experience is paused, to purchase a copy of the art image or,where original art is available by permission of the artist or artist'srepresentation, the original art piece. Whether the user indicates apurchase by clicking the BUY button or decides against a purchase atthat time, the user may select the RESUME STREAM 1004 text fieldsuperimposed on the art imagery to resume streaming art imagery on thedisplay device. The Pause, BUY, and RESUME STREAM steps illustrate theseamless marketplace integration with an art streaming experience of theLoupe display system.

While certain illustrative embodiments have been described, it isevident that many alternatives, modifications, permutations andvariations will become apparent to those skilled in the art in light ofthe foregoing description.

We claim:
 1. A method for optimizing image viewing and interaction,comprising: providing a plurality of images to a streaming presentationsystem, wherein: (i) the plurality of images are selected forpresentation based at least in part on one or more initial imagepresentation attributes, (ii) each image is associated with an imageattribute set defined in accordance with an attribute taxonomy, and(iii) the streaming presentation system is configured to present theplurality of images using an image stream; receiving collected userinteraction data relating to a plurality of user actions performed inrelation to the streaming presentation system during presentation of theplurality of images, wherein the plurality of user actions comprise: (i)one or more image pause actions, or (ii) one or more image replayactions; for each image, determining a user interest score based atleast in part on the plurality of user actions; determining, based atleast in part on each user interest score, a user preference subset ofthe plurality of images and a user non-preference subset of theplurality of images; modifying, based at least in part on each imageattribute set for the user preference subset and each image attributeset for the user non-preference subset, the one or more initial imagepresentation attributes to generate one or more updated imagepresentation attributes; selecting one or more additional images basedat least in part on the one or more updated image presentationattributes; and providing data describing the one or more additionalimages to the streaming presentation system for presentation using thestreaming presentation system, wherein the streaming presentation systemis configured to present the one or more additional images to an enduser.
 2. The method of claim 1, further comprising collecting user visitand interaction data regarding user visit and interaction with theplurality of images over time and storing said user visit andinteraction data in an electronic data base.
 3. The method of claim 2wherein said user visit and interaction data is analyzed to provide avisit data parameter associated with an amount of time any user isactively interacting with a currently-displayed image.
 4. The method ofclaim 3 wherein said visit data parameter is associated with aparticular user and a particular set of image attributes.
 5. The methodof claim 4 wherein the visit data parameter is compared against apreviously stored visit data parameter and if the visit data parameterexceeds the previously stored visit data parameter, the particular setof image attributes is updated to capture new image attribute values forsaid particular set of image attributes to optimize viewer interactionwith the plurality of images.
 6. The method of claim 1, furthercomprising creating a channel for displaying the plurality of imagesbased at least in part on each image attribute set.
 7. The method ofclaim 6, wherein said channel is created as a private streaming channelcreated and provisioned for one or more users and/or one or morecommercial spaces.
 8. The method of claim 1 wherein users of thestreaming presentation system are provided with an option to interactwith each image to purchase the image, save the image for purchase,download the image, and/or mark the image as a favorite image.
 9. Themethod of claim 8, wherein purchasing a currently-displayed imagecomprises facilitating purchasing of fine art reproductions andoriginals of the currently-displayed image.
 10. A system for optimizingimage viewing and interaction, the system comprising at least oneprocessor and at least one memory including program code, the at leastone memory and the program code configured to, with the at least oneprocessor, cause the system to at least: provide a plurality of imagesto a streaming presentation system, wherein: (i) the plurality of imagesare selected for presentation based at least in part on one or moreinitial image presentation attributes, (ii) each image is associatedwith an image attribute set defined in accordance with an attributetaxonomy, and (iii) the streaming presentation system is configured topresent the plurality of images using an image stream; receive collecteduser interaction data relating to a plurality of user actions performedin relation to the streaming presentation system during presentation ofthe plurality of images, wherein the plurality of user actions comprise:(i) one or more image pause actions, or (ii) one or more image replayactions; for each image, determine a user interest score based at leastin part on the plurality of user actions; determine, based at least inpart on each user interest score, a user preference subset of theplurality of images and a user non-preference subset of the plurality ofimages; modify, based at least in part on each image attribute set forthe user preference subset and each image attribute set for the usernon-preference subset, the one or more initial image presentationattributes to generate one or more updated image presentationattributes; select one or more additional images based at least in parton the one or more updated image presentation attributes; and providedata describing the one or more additional images to the streamingpresentation system for presentation using the streaming presentationsystem, wherein the streaming presentation system is configured topresent the one or more additional images to an end user.
 11. The systemof claim 10, the at least one memory and the program code are furtherconfigured to, with the at least one processor, cause the system to atleast collect user visit and interaction data regarding user visit andinteraction with the plurality of images over time and storing said uservisit and interaction data in an electronic data base.
 12. The system ofclaim 11 wherein said user visit and interaction data is analyzed toprovide a visit data parameter associated with an amount of time anyuser is actively interacting with a currently-displayed image.
 13. Thesystem of claim 12 wherein said visit data parameter is associated witha particular user and a particular set of image attributes.
 14. Thesystem of claim 13 wherein the visit data parameter is compared againsta previously stored visit data parameter and if the visit data parameterexceeds the previously stored visit data parameter, the particular setof image attributes is updated to capture new image attribute values forsaid particular set of image attributes to optimize viewer interactionwith the plurality of images.
 15. The system of claim 10, furthercomprising creating a channel for displaying the plurality of imagesbased at least in part on each image attribute set.
 16. The system ofclaim 15, wherein said is created as a private streaming channel createdand provisioned for one or more users and/or one or more commercialspaces.
 17. The system of claim 10 wherein users of the streamingpresentation system are provided with an option to interact with eachimage to purchase the image, save the image for purchase, download theimage, and/or mark the image as a favorite image, and otherwise interactwith imagery selected by the users.
 18. The system of claim 17, whereinpurchasing a currently-displayed image comprises facilitating purchasingof fine art reproductions and originals the currently-displayed image.